DESCRIPTION: Dr. Nancy A. Baker, ScD, the candidate for this Special Emphasis Research Career Award, is an Assistant Professor in the School of Health and Rehabilitation Sciences (SHRS) at the University of Pittsburgh. Her immediate career goals are to develop her skills as an independent researcher and improve her expertise in biomechanical theory, test development, epidemiology, and statistical analysis through mentored research and coursework. She plans to apply for an R01 award with the data acquired during this study. Her long term career goal is to develop a research program which will evaluate clinical interventions for preventing and remediating musculoskeletal disorders of the upper extremity (MSD-UE) in computer users. This proposed three-year study will be divided into two phases. Phase I will be devoted to developing and refining an instrument, the Pre-PeCKS, into a valid and reliable data gathering observational tool. /n Phase 1I the Pre-PeCKS will be used to develop a predictive model that can discriminate between those with and without MSD-UE. The model developed will be used to select the construction and weighting of the subscales of the final instrument, the PECKS. The specific aim of Phase I is to evaluate the inter- and intra- rater reliability and the concurrent criterion-related validity of the Pre-PeCKS; the specific aim of Phase II is to use information gathered using the Pre-PeCKS to develop a diagnostic instrument that can identify those with and without MSD-UE. In Phase I, 50 subjects will be digitally recorded while typing while at the same time the kinematics of their hands will be recorded using a VICON TM motion analysis system and a keyboard force plate. The video of the typing performance will be rated by independent raters using the Pre-PeCKS and reliability statistics will be calculated. Concurrent validity will be obtained by comparing the results of the VICON TM with the results of the Pre-PECKS. In Phase 11, 20 typists with MSD-UE and 20 typists without MSD-UE will be rated using the Pre-PeCKS. Variables from the Pre-PeCKS along with other variables of interest will be used to build models that can discriminate between those with and without MSD-UE. The best model will be used to assign weights to Pre-PeCKS parameters and to develop how subscores will be combined to best identify those with MSD-UE. This information will be integrated into the final tool, the PECKS. The PECKS can then be used to identify an individual who have keyboarding...styles that have been associated with MSD-UE.